Pith. sign in

REVIEW 1 cited by

JuTrack: a Julia package for auto-differentiable accelerator modeling and particle tracking

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2409.20522 v2 pith:4XEBIMXM submitted 2024-09-30 physics.acc-ph

classification physics.acc-ph
keywords acceleratormodelingjutrackpackageparticleanalysisautomaticbeam
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Efficient accelerator modeling and particle tracking are key for the design and configuration of modern particle accelerators. In this work, we present JuTrack, a nested accelerator modeling package developed in the Julia programming language and enhanced with compiler-level automatic differentiation (AD). With the aid of AD, JuTrack enables rapid derivative calculations in accelerator modeling, facilitating sensitivity analyses and optimization tasks. We demonstrate the effectiveness of AD-derived derivatives through several practical applications, including sensitivity analysis of space-charge-induced emittance growth, nonlinear beam dynamics analysis for a synchrotron light source, and lattice parameter tuning of the future Electron-Ion Collider (EIC). Through the incorporation of automatic differentiation, this package opens up new possibilities for accelerator physicists in beam physics studies and accelerator design optimization.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Differentiable simulations for particle tracking in accelerators: analysis, benchmarking and optimization

    physics.acc-ph 2025-07 conditional novelty 6.0 of 10

    Automatic differentiation computes beamline optimization gradients faster than finite differences on CPU and usually on GPU.

Pith tools